/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    SMOreg.java
 *    Copyright (C) 2006 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.functions;

import weka.classifiers.Classifier;
import weka.classifiers.functions.supportVector.Kernel;
import weka.classifiers.functions.supportVector.PolyKernel;
import weka.classifiers.functions.supportVector.RegOptimizer;
import weka.classifiers.functions.supportVector.RegSMOImproved;
import weka.core.AdditionalMeasureProducer;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.NominalToBinary;
import weka.filters.unsupervised.attribute.Normalize;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;
import weka.filters.unsupervised.attribute.Standardize;

import java.util.Enumeration;
import java.util.Vector;

/**
 * <!-- globalinfo-start --> SMOreg implements the support vector machine for
 * regression. The parameters can be learned using various algorithms. The
 * algorithm is selected by setting the RegOptimizer. The most popular algorithm
 * (RegSMOImproved) is due to Shevade, Keerthi et al and this is the default
 * RegOptimizer.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to
 * the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural
 * Networks, 1999.<br/>
 * <br/>
 * A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- technical-bibtex-start --> BibTeX:
 * 
 * <pre>
 * &#64;inproceedings{Shevade1999,
 *    author = {S.K. Shevade and S.S. Keerthi and C. Bhattacharyya and K.R.K. Murthy},
 *    booktitle = {IEEE Transactions on Neural Networks},
 *    title = {Improvements to the SMO Algorithm for SVM Regression},
 *    year = {1999},
 *    PS = {http://guppy.mpe.nus.edu.sg/\~mpessk/svm/ieee_smo_reg.ps.gz}
 * }
 * 
 * &#64;techreport{Smola1998,
 *    author = {A.J. Smola and B. Schoelkopf},
 *    note = {NeuroCOLT2 Technical Report NC2-TR-1998-030},
 *    title = {A tutorial on support vector regression},
 *    year = {1998}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -C &lt;double&gt;
 *  The complexity constant C.
 *  (default 1)
 * </pre>
 * 
 * <pre>
 * -N
 *  Whether to 0=normalize/1=standardize/2=neither.
 *  (default 0=normalize)
 * </pre>
 * 
 * <pre>
 * -I &lt;classname and parameters&gt;
 *  Optimizer class used for solving quadratic optimization problem
 *  (default weka.classifiers.functions.supportVector.RegSMOImproved)
 * </pre>
 * 
 * <pre>
 * -K &lt;classname and parameters&gt;
 *  The Kernel to use.
 *  (default: weka.classifiers.functions.supportVector.PolyKernel)
 * </pre>
 * 
 * <pre>
 * Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:
 * </pre>
 * 
 * <pre>
 * -T &lt;double&gt;
 *  The tolerance parameter for checking the stopping criterion.
 *  (default 0.001)
 * </pre>
 * 
 * <pre>
 * -V
 *  Use variant 1 of the algorithm when true, otherwise use variant 2.
 *  (default true)
 * </pre>
 * 
 * <pre>
 * -P &lt;double&gt;
 *  The epsilon for round-off error.
 *  (default 1.0e-12)
 * </pre>
 * 
 * <pre>
 * -L &lt;double&gt;
 *  The epsilon parameter in epsilon-insensitive loss function.
 *  (default 1.0e-3)
 * </pre>
 * 
 * <pre>
 * -W &lt;double&gt;
 *  The random number seed.
 *  (default 1)
 * </pre>
 * 
 * <pre>
 * Options specific to kernel ('-K') weka.classifiers.functions.supportVector.PolyKernel:
 * </pre>
 * 
 * <pre>
 * -D
 *  Enables debugging output (if available) to be printed.
 *  (default: off)
 * </pre>
 * 
 * <pre>
 * -no-checks
 *  Turns off all checks - use with caution!
 *  (default: checks on)
 * </pre>
 * 
 * <pre>
 * -C &lt;num&gt;
 *  The size of the cache (a prime number), 0 for full cache and 
 *  -1 to turn it off.
 *  (default: 250007)
 * </pre>
 * 
 * <pre>
 * -E &lt;num&gt;
 *  The Exponent to use.
 *  (default: 1.0)
 * </pre>
 * 
 * <pre>
 * -L
 *  Use lower-order terms.
 *  (default: no)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Remco Bouckaert (remco@cs.waikato.ac.nz,rrb@xm.co.nz)
 * @version $Revision: 5439 $
 */
public class SMOreg extends Classifier implements WeightedInstancesHandler,
		AdditionalMeasureProducer, TechnicalInformationHandler {

	/** for serialization */
	private static final long serialVersionUID = -7149606251113102827L;

	/** The filter to apply to the training data: Normalzie */
	public static final int FILTER_NORMALIZE = 0;
	/** The filter to apply to the training data: Standardize */
	public static final int FILTER_STANDARDIZE = 1;
	/** The filter to apply to the training data: None */
	public static final int FILTER_NONE = 2;
	/** The filter to apply to the training data */
	public static final Tag[] TAGS_FILTER = {
			new Tag(FILTER_NORMALIZE, "Normalize training data"),
			new Tag(FILTER_STANDARDIZE, "Standardize training data"),
			new Tag(FILTER_NONE, "No normalization/standardization"), };

	/** Whether to normalize/standardize/neither */
	protected int m_filterType = FILTER_NORMALIZE;

	/** The filter used to make attributes numeric. */
	protected NominalToBinary m_NominalToBinary;

	/** The filter used to standardize/normalize all values. */
	protected Filter m_Filter = null;

	/** The filter used to get rid of missing values. */
	protected ReplaceMissingValues m_Missing;

	/** Only numeric attributes in the dataset? If so, less need to filter */
	protected boolean m_onlyNumeric;

	/** capacity parameter **/
	protected double m_C = 1.0;

	/**
	 * coefficients used by normalization filter for doing its linear
	 * transformation so that result = svmoutput * m_x1 + m_x0
	 **/
	protected double m_x1 = 1.0;
	protected double m_x0 = 0.0;

	/** contains the algorithm used for learning **/
	protected RegOptimizer m_optimizer = new RegSMOImproved();

	/** the configured kernel */
	protected Kernel m_kernel = new PolyKernel();

	/**
	 * Returns a string describing classifier
	 * 
	 * @return a description suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String globalInfo() {
		return "SMOreg implements the support vector machine for regression. "
				+ "The parameters can be learned using various algorithms. The "
				+ "algorithm is selected by setting the RegOptimizer. The most "
				+ "popular algorithm ("
				+ RegSMOImproved.class.getName().replaceAll(".*\\.", "")
				+ ") is due to Shevade, Keerthi "
				+ "et al and this is the default RegOptimizer.\n\n"
				+ "For more information see:\n\n"
				+ getTechnicalInformation().toString();
	}

	/**
	 * Returns an instance of a TechnicalInformation object, containing detailed
	 * information about the technical background of this class, e.g., paper
	 * reference or book this class is based on.
	 * 
	 * @return the technical information about this class
	 */
	public TechnicalInformation getTechnicalInformation() {
		TechnicalInformation result;
		TechnicalInformation additional;

		result = new TechnicalInformation(Type.INPROCEEDINGS);
		result.setValue(Field.AUTHOR,
				"S.K. Shevade and S.S. Keerthi and C. Bhattacharyya and K.R.K. Murthy");
		result.setValue(Field.TITLE,
				"Improvements to the SMO Algorithm for SVM Regression");
		result.setValue(Field.BOOKTITLE, "IEEE Transactions on Neural Networks");
		result.setValue(Field.YEAR, "1999");
		result.setValue(Field.PS,
				"http://guppy.mpe.nus.edu.sg/~mpessk/svm/ieee_smo_reg.ps.gz");

		additional = result.add(Type.TECHREPORT);
		additional.setValue(Field.AUTHOR, "A.J. Smola and B. Schoelkopf");
		additional.setValue(Field.TITLE,
				"A tutorial on support vector regression");
		additional.setValue(Field.NOTE,
				"NeuroCOLT2 Technical Report NC2-TR-1998-030");
		additional.setValue(Field.YEAR, "1998");

		return result;
	}

	/**
	 * Returns an enumeration describing the available options.
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {
		Enumeration enm;
		Vector result = new Vector();

		result.addElement(new Option("\tThe complexity constant C.\n"
				+ "\t(default 1)", "C", 1, "-C <double>"));

		result.addElement(new Option(
				"\tWhether to 0=normalize/1=standardize/2=neither.\n"
						+ "\t(default 0=normalize)", "N", 1, "-N"));

		result.addElement(new Option(
				"\tOptimizer class used for solving quadratic optimization problem\n"
						+ "\t(default " + RegSMOImproved.class.getName() + ")",
				"I", 1, "-I <classname and parameters>"));

		result.addElement(new Option(
				"\tThe Kernel to use.\n"
						+ "\t(default: weka.classifiers.functions.supportVector.PolyKernel)",
				"K", 1, "-K <classname and parameters>"));

		result.addElement(new Option("", "", 0,
				"\nOptions specific to optimizer ('-I') "
						+ getRegOptimizer().getClass().getName() + ":"));

		enm = ((OptionHandler) getRegOptimizer()).listOptions();
		while (enm.hasMoreElements())
			result.addElement(enm.nextElement());

		result.addElement(new Option("", "", 0,
				"\nOptions specific to kernel ('-K') "
						+ getKernel().getClass().getName() + ":"));

		enm = ((OptionHandler) getKernel()).listOptions();
		while (enm.hasMoreElements())
			result.addElement(enm.nextElement());

		return result.elements();
	}

	/**
	 * Parses a given list of options.
	 * <p/>
	 * 
	 * <!-- options-start --> Valid options are:
	 * <p/>
	 * 
	 * <pre>
	 * -C &lt;double&gt;
	 *  The complexity constant C.
	 *  (default 1)
	 * </pre>
	 * 
	 * <pre>
	 * -N
	 *  Whether to 0=normalize/1=standardize/2=neither.
	 *  (default 0=normalize)
	 * </pre>
	 * 
	 * <pre>
	 * -I &lt;classname and parameters&gt;
	 *  Optimizer class used for solving quadratic optimization problem
	 *  (default weka.classifiers.functions.supportVector.RegSMOImproved)
	 * </pre>
	 * 
	 * <pre>
	 * -K &lt;classname and parameters&gt;
	 *  The Kernel to use.
	 *  (default: weka.classifiers.functions.supportVector.PolyKernel)
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:
	 * </pre>
	 * 
	 * <pre>
	 * -T &lt;double&gt;
	 *  The tolerance parameter for checking the stopping criterion.
	 *  (default 0.001)
	 * </pre>
	 * 
	 * <pre>
	 * -V
	 *  Use variant 1 of the algorithm when true, otherwise use variant 2.
	 *  (default true)
	 * </pre>
	 * 
	 * <pre>
	 * -P &lt;double&gt;
	 *  The epsilon for round-off error.
	 *  (default 1.0e-12)
	 * </pre>
	 * 
	 * <pre>
	 * -L &lt;double&gt;
	 *  The epsilon parameter in epsilon-insensitive loss function.
	 *  (default 1.0e-3)
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;double&gt;
	 *  The random number seed.
	 *  (default 1)
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to kernel ('-K') weka.classifiers.functions.supportVector.PolyKernel:
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 *  Enables debugging output (if available) to be printed.
	 *  (default: off)
	 * </pre>
	 * 
	 * <pre>
	 * -no-checks
	 *  Turns off all checks - use with caution!
	 *  (default: checks on)
	 * </pre>
	 * 
	 * <pre>
	 * -C &lt;num&gt;
	 *  The size of the cache (a prime number), 0 for full cache and 
	 *  -1 to turn it off.
	 *  (default: 250007)
	 * </pre>
	 * 
	 * <pre>
	 * -E &lt;num&gt;
	 *  The Exponent to use.
	 *  (default: 1.0)
	 * </pre>
	 * 
	 * <pre>
	 * -L
	 *  Use lower-order terms.
	 *  (default: no)
	 * </pre>
	 * 
	 * <!-- options-end -->
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @throws Exception
	 *             if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {
		String tmpStr;
		String[] tmpOptions;

		tmpStr = Utils.getOption('C', options);
		if (tmpStr.length() != 0) {
			setC(Double.parseDouble(tmpStr));
		} else {
			setC(1.0);
		}

		String nString = Utils.getOption('N', options);
		if (nString.length() != 0) {
			setFilterType(new SelectedTag(Integer.parseInt(nString),
					TAGS_FILTER));
		} else {
			setFilterType(new SelectedTag(FILTER_NORMALIZE, TAGS_FILTER));
		}

		tmpStr = Utils.getOption('I', options);
		tmpOptions = Utils.splitOptions(tmpStr);
		if (tmpOptions.length != 0) {
			tmpStr = tmpOptions[0];
			tmpOptions[0] = "";
			setRegOptimizer((RegOptimizer) Utils.forName(RegOptimizer.class,
					tmpStr, tmpOptions));
		} else {
			setRegOptimizer(new RegSMOImproved());
		}

		tmpStr = Utils.getOption('K', options);
		tmpOptions = Utils.splitOptions(tmpStr);
		if (tmpOptions.length != 0) {
			tmpStr = tmpOptions[0];
			tmpOptions[0] = "";
			setKernel(Kernel.forName(tmpStr, tmpOptions));
		} else {
			setKernel(new PolyKernel());
		}
	}

	/**
	 * Gets the current settings of the classifier.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {
		int i;
		Vector result;
		String[] options;

		result = new Vector();

		options = super.getOptions();
		for (i = 0; i < options.length; i++)
			result.add(options[i]);

		result.add("-C");
		result.add("" + getC());

		result.add("-N");
		result.add("" + m_filterType);

		result.add("-I");
		result.add("" + getRegOptimizer().getClass().getName() + " "
				+ Utils.joinOptions(getRegOptimizer().getOptions()));

		result.add("-K");
		result.add("" + getKernel().getClass().getName() + " "
				+ Utils.joinOptions(getKernel().getOptions()));

		return (String[]) result.toArray(new String[result.size()]);
	}

	/**
	 * Returns default capabilities of the classifier.
	 * 
	 * @return the capabilities of this classifier
	 */
	public Capabilities getCapabilities() {
		Capabilities result = getKernel().getCapabilities();
		result.setOwner(this);

		// attribute
		result.enableAllAttributeDependencies();
		// with NominalToBinary we can also handle nominal attributes, but only
		// if the kernel can handle numeric attributes
		if (result.handles(Capability.NUMERIC_ATTRIBUTES))
			result.enable(Capability.NOMINAL_ATTRIBUTES);
		result.enable(Capability.MISSING_VALUES);

		// class
		result.disableAllClasses();
		result.disableAllClassDependencies();
		result.enable(Capability.NUMERIC_CLASS);
		result.enable(Capability.DATE_CLASS);
		result.enable(Capability.MISSING_CLASS_VALUES);

		return result;
	}

	/**
	 * Method for building the classifier.
	 * 
	 * @param instances
	 *            the set of training instances
	 * @throws Exception
	 *             if the classifier can't be built successfully
	 */
	public void buildClassifier(Instances instances) throws Exception {
		// can classifier handle the data?
		getCapabilities().testWithFail(instances);

		// remove instances with missing class
		instances = new Instances(instances);
		instances.deleteWithMissingClass();

		// Removes all the instances with weight equal to 0.
		// MUST be done since condition (8) of Keerthi's paper
		// is made with the assertion Ci > 0 (See equation (3a).
		Instances data = new Instances(instances, 0);
		for (int i = 0; i < instances.numInstances(); i++) {
			if (instances.instance(i).weight() > 0) {
				data.add(instances.instance(i));
			}
		}

		if (data.numInstances() == 0) {
			throw new Exception("No training instances left after removing "
					+ "instance with either a weight null or a missing class!");
		}
		instances = data;

		m_onlyNumeric = true;
		for (int i = 0; i < instances.numAttributes(); i++) {
			if (i != instances.classIndex()) {
				if (!instances.attribute(i).isNumeric()) {
					m_onlyNumeric = false;
					break;
				}
			}
		}
		m_Missing = new ReplaceMissingValues();
		m_Missing.setInputFormat(instances);
		instances = Filter.useFilter(instances, m_Missing);

		if (!m_onlyNumeric) {
			m_NominalToBinary = new NominalToBinary();
			m_NominalToBinary.setInputFormat(instances);
			instances = Filter.useFilter(instances, m_NominalToBinary);
		} else {
			m_NominalToBinary = null;
		}

		// retrieve two different class values used to determine filter
		// transformation
		double y0 = instances.instance(0).classValue();
		int index = 1;
		while (index < instances.numInstances()
				&& instances.instance(index).classValue() == y0) {
			index++;
		}
		if (index == instances.numInstances()) {
			// degenerate case, all class values are equal
			// we don't want to deal with this, too much hassle
			throw new Exception(
					"All class values are the same. At least two class values should be different");
		}
		double y1 = instances.instance(index).classValue();

		// apply filters
		if (m_filterType == FILTER_STANDARDIZE) {
			m_Filter = new Standardize();
			((Standardize) m_Filter).setIgnoreClass(true);
			m_Filter.setInputFormat(instances);
			instances = Filter.useFilter(instances, m_Filter);
		} else if (m_filterType == FILTER_NORMALIZE) {
			m_Filter = new Normalize();
			((Normalize) m_Filter).setIgnoreClass(true);
			m_Filter.setInputFormat(instances);
			instances = Filter.useFilter(instances, m_Filter);
		} else {
			m_Filter = null;
		}
		if (m_Filter != null) {
			double z0 = instances.instance(0).classValue();
			double z1 = instances.instance(index).classValue();
			m_x1 = (y0 - y1) / (z0 - z1); // no division by zero, since y0 != y1
											// guaranteed => z0 != z1 ???
			m_x0 = (y0 - m_x1 * z0); // = y1 - m_x1 * z1
		} else {
			m_x1 = 1.0;
			m_x0 = 0.0;
		}

		m_optimizer.setSMOReg(this);
		m_optimizer.buildClassifier(instances);
	}

	/**
	 * Classifies the given instance using the linear regression function.
	 * 
	 * @param instance
	 *            the test instance
	 * @return the classification
	 * @throws Exception
	 *             if classification can't be done successfully
	 */
	public double classifyInstance(Instance instance) throws Exception {
		// Filter instance
		m_Missing.input(instance);
		m_Missing.batchFinished();
		instance = m_Missing.output();

		if (!m_onlyNumeric) {
			m_NominalToBinary.input(instance);
			m_NominalToBinary.batchFinished();
			instance = m_NominalToBinary.output();
		}

		if (m_Filter != null) {
			m_Filter.input(instance);
			m_Filter.batchFinished();
			instance = m_Filter.output();
		}

		double result = m_optimizer.SVMOutput(instance);
		return result * m_x1 + m_x0;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String regOptimizerTipText() {
		return "The learning algorithm.";
	}

	/**
	 * sets the learning algorithm
	 * 
	 * @param regOptimizer
	 *            the learning algorithm
	 */
	public void setRegOptimizer(RegOptimizer regOptimizer) {
		m_optimizer = regOptimizer;
	}

	/**
	 * returns the learning algorithm
	 * 
	 * @return the learning algorithm
	 */
	public RegOptimizer getRegOptimizer() {
		return m_optimizer;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String kernelTipText() {
		return "The kernel to use.";
	}

	/**
	 * sets the kernel to use
	 * 
	 * @param value
	 *            the kernel to use
	 */
	public void setKernel(Kernel value) {
		m_kernel = value;
	}

	/**
	 * Returns the kernel to use
	 * 
	 * @return the current kernel
	 */
	public Kernel getKernel() {
		return m_kernel;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String cTipText() {
		return "The complexity parameter C.";
	}

	/**
	 * Get the value of C.
	 * 
	 * @return Value of C.
	 */
	public double getC() {
		return m_C;
	}

	/**
	 * Set the value of C.
	 * 
	 * @param v
	 *            Value to assign to C.
	 */
	public void setC(double v) {
		m_C = v;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String filterTypeTipText() {
		return "Determines how/if the data will be transformed.";
	}

	/**
	 * Gets how the training data will be transformed. Will be one of
	 * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
	 * 
	 * @return the filtering mode
	 */
	public SelectedTag getFilterType() {
		return new SelectedTag(m_filterType, TAGS_FILTER);
	}

	/**
	 * Sets how the training data will be transformed. Should be one of
	 * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
	 * 
	 * @param newType
	 *            the new filtering mode
	 */
	public void setFilterType(SelectedTag newType) {
		if (newType.getTags() == TAGS_FILTER) {
			m_filterType = newType.getSelectedTag().getID();
		}
	}

	/**
	 * Prints out the classifier.
	 * 
	 * @return a description of the classifier as a string
	 */
	public String toString() {
		StringBuffer text = new StringBuffer();

		if (m_optimizer == null || !m_optimizer.modelBuilt()) {
			return "SMOreg: No model built yet.";
		}

		try {
			text.append(m_optimizer.toString());
		} catch (Exception e) {
			return "Can't print SMVreg classifier.";
		}

		return text.toString();
	}

	/**
	 * Returns an enumeration of the measure names. Additional measures must
	 * follow the naming convention of starting with "measure", eg. double
	 * measureBlah()
	 * 
	 * @return an enumeration of the measure names
	 */
	public Enumeration enumerateMeasures() {
		Vector result = new Vector();

		result.addElement("measureKernelEvaluations");
		result.addElement("measureCacheHits");

		return result.elements();
	}

	/**
	 * Returns the value of the named measure
	 * 
	 * @param measureName
	 *            the name of the measure to query for its value
	 * @return the value of the named measure
	 * @throws IllegalArgumentException
	 *             if the named measure is not supported
	 */
	public double getMeasure(String measureName) {
		if (measureName.equals("measureKernelEvaluations"))
			return measureKernelEvaluations();
		else if (measureName.equals("measureCacheHits"))
			return measureCacheHits();
		else
			throw new IllegalArgumentException("Measure '" + measureName
					+ "' is not supported!");
	}

	/**
	 * number of kernel evaluations used in learing
	 * 
	 * @return the number of kernel evaluations
	 */
	protected double measureKernelEvaluations() {
		if (m_optimizer != null) {
			return m_optimizer.getKernelEvaluations();
		} else {
			return 0;
		}
	}

	/**
	 * number of kernel cache hits used during learing
	 * 
	 * @return the number of kernel cache hits
	 */
	protected double measureCacheHits() {
		if (m_optimizer != null) {
			return m_optimizer.getCacheHits();
		} else {
			return 0;
		}
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 5439 $");
	}

	/**
	 * Main method for running this classifier.
	 * 
	 * @param args
	 *            the commandline options
	 */
	public static void main(String[] args) {
		runClassifier(new SMOreg(), args);
	}
}
